Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	36,669
2	Mississippi	34,164
3	Florida	33,528
4	Alabama	32,729
5	North Dakota	31,976
6	Arizona	30,491
7	Iowa	29,764
8	South Carolina	29,710
9	Tennessee	29,616
10	Arkansas	29,183
11	Georgia	29,055
12	South Dakota	28,119
13	Texas	27,958
14	Nevada	26,958
15	Idaho	25,453
16	Nebraska	25,205
17	Utah	24,781
18	Wisconsin	24,712
19	Illinois	24,384
20	New York	24,244
21	Rhode Island	24,161
22	New Jersey	23,769
23	Oklahoma	23,590
24	Missouri	22,675
25	District of Columbia	22,177
26	Delaware	22,044
27	Kansas	21,975
28	Maryland	21,286
29	California	21,267
30	North Carolina	21,113
31	Massachusetts	19,725
32	Indiana	19,203
33	Minnesota	18,758
34	Virginia	17,946
35	Kentucky	17,400
36	Connecticut	16,616
37	Puerto Rico	16,199
38	New Mexico	14,758
39	Montana	14,626
40	Michigan	14,409
41	Ohio	13,799
42	Pennsylvania	13,299
43	Alaska	13,199
44	Colorado	12,942
45	Washington	12,435
46	Wyoming	11,697
47	West Virginia	9,450
48	Hawaii	9,245
49	Oregon	8,380
50	New Hampshire	6,421
51	Maine	4,139
52	Vermont	2,918

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	538
2	Montana	356
3	South Dakota	335
4	Wisconsin	335
5	Utah	334
6	Idaho	332
7	Tennessee	262
8	Alaska	248
9	Missouri	236
10	Nebraska	233
11	Wyoming	233
12	Oklahoma	218
13	Kansas	185
14	Minnesota	174
15	Arkansas	170
16	Iowa	169
17	Mississippi	169
18	Rhode Island	163
19	Kentucky	161
20	Alabama	142
21	Puerto Rico	142
22	Indiana	141
23	North Carolina	139
24	Illinois	136
25	South Carolina	134
26	Texas	131
27	Nevada	130
28	Louisiana	117
29	Delaware	116
30	New Mexico	103
31	Pennsylvania	103
32	Ohio	95
33	Colorado	92
34	Virginia	92
35	Connecticut	88
36	Michigan	88
37	West Virginia	87
38	Florida	85
39	District of Columbia	84
40	Maryland	82
41	Georgia	80
42	Massachusetts	79
43	California	74
44	Arizona	70
45	Washington	67
46	New Jersey	66
47	Oregon	65
48	New York	60
49	Hawaii	47
50	New Hampshire	32
51	Vermont	22
52	Maine	19

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,817
2	New York	1,687
3	Massachusetts	1,383
4	Connecticut	1,268
5	Louisiana	1,202
6	Rhode Island	1,061
7	Mississippi	1,017
8	District of Columbia	894
9	Arizona	784
10	Illinois	718
11	Michigan	717
12	Florida	687
13	South Carolina	674
14	Georgia	665
15	Delaware	663
16	Maryland	656
17	Pennsylvania	649
18	Texas	573
19	Indiana	551
20	Nevada	529
21	Alabama	526
22	Arkansas	486
23	Iowa	448
24	New Mexico	426
25	Ohio	423
26	California	411
27	Virginia	385
28	Minnesota	379
29	Tennessee	379
30	North Dakota	372
31	Missouri	367
32	Colorado	363
33	North Carolina	351
34	New Hampshire	328
35	Washington	296
36	Kentucky	280
37	South Dakota	280
38	Idaho	276
39	Oklahoma	269
40	Nebraska	264
41	Kansas	242
42	Wisconsin	242
43	Puerto Rico	217
44	West Virginia	203
45	Montana	179
46	Utah	152
47	Oregon	137
48	Hawaii	112
49	Maine	105
50	Vermont	92
51	Wyoming	91
52	Alaska	73

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arkansas	6
2	North Dakota	4
3	Iowa	3
4	Louisiana	3
5	Florida	2
6	Georgia	2
7	Idaho	2
8	Indiana	2
9	Missouri	2
10	Rhode Island	2
11	Tennessee	2
12	Texas	2
13	Alabama	1
14	California	1
15	Colorado	1
16	Hawaii	1
17	Illinois	1
18	Kentucky	1
19	Massachusetts	1
20	Michigan	1
21	Mississippi	1
22	Montana	1
23	Nebraska	1
24	North Carolina	1
25	Oklahoma	1
26	Pennsylvania	1
27	Puerto Rico	1
28	South Carolina	1
29	Utah	1
30	Washington	1
31	West Virginia	1
32	Wisconsin	1
33	Alaska	0
34	Arizona	0
35	Connecticut	0
36	Delaware	0
37	District of Columbia	0
38	Kansas	0
39	Maine	0
40	Maryland	0
41	Minnesota	0
42	Nevada	0
43	New Hampshire	0
44	New Jersey	0
45	New Mexico	0
46	New York	0
47	Ohio	0
48	Oregon	0
49	South Dakota	0
50	Vermont	0
51	Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	160,703	1	99
Chattahoochee	Georgia	154,855	2	99
Trousdale	Tennessee	150,390	3	99
Lafayette	Florida	146,758	4	99
Lake	Tennessee	133,694	5	99
Davidson	Tennessee	42,686	207	93
Richland	South Carolina	38,111	295	90
York	South Carolina	20,279	1239	60
Orange	California	17,789	1464	53
Pierce	Washington	10,271	2300	26

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,085	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	604	723	76
Davidson	Tennessee	457	1021	67
Orange	California	406	1142	63
York	South Carolina	299	1432	54
Pierce	Washington	253	1586	49

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.